ES2741963T3 - Audio signal encoders, methods for encoding an audio signal and software - Google Patents

Abstract

Audio encoder for generating an encoded audio signal, comprising: an audio signal analyzer (516, 520) for analyzing whether a time frame of the audio signal has a harmonic or expression characteristic; a window function controller (504) for selecting a window function depending on an harmonic or expression characteristic of the audio signal; a window generator (502) for generating windows of the audio signal using the selected window function to obtain a windowed frame; and a processor (508, 512) to further process the windowed frame to obtain the encoded audio signal; wherein the window function controller (504) comprises a transient detector (700) for detecting a transient, wherein the window function controller is configured to switch from a window function for a large block to a function window for a short block, when a transient is detected and the audio signal analyzer (516, 520) cannot find a harmonic or expression characteristic, and not to switch to the window function for the short block, when it is detected a transient and the audio signal analyzer (516, 520) finds a harmonic or expression characteristic; and wherein the window function controller (504) is configured to switch from a window function (706) that has a sample length 2048 as the long block, but that has a zero portion (708) and a portion ( 710) of not aliasing to a window function (707) that is longer than the window function for a short block and is adapted to obtain a shorter overlap length (712) with a window (706) prior to the function (714) window for a long block and having a zero portion to the left of a region (712) and a non-aliasing portion to the right of the region (712), when a transient is detected and the signal has a harmonic or expression characteristic, so there is an overlap portion between 1/4 and 1/32 of the full window function length.

Description

DESCRIPTION

Audio signal encoders, methods for encoding an audio signal and software

The present invention relates to audio coding and decoding and specifically to the coding / decoding of the audio signal having a harmonic or expression content, which can be subjected to time warp distortion processing.

Next, a brief introduction to the field of time-distorted audio coding will be given, the concepts of which can be applied in conjunction with some of the embodiments of the invention.

In recent years, techniques have been developed to transform an audio signal into a frequency domain representation, and to efficiently encode this frequency domain representation, for example taking into account perceptual masking thresholds. This concept of audio signal coding is particularly efficient if the block length, for which a set of coded spectral coefficients is transmitted, is long, and if only a relatively small number of spectral coefficients are well above the masking threshold. global while a large number of spectral coefficients is near or below the global masking threshold and thus can be insignificant (or coded with the minimum code length).

For example, sine or cosine modulated overlay transforms are often used in applications for source coding due to their energy compaction properties. That is, for harmonic tones with constant fundamental frequencies (tone), they concentrate the signal energy to a low number of spectral components (sub-bands), which leads to an efficient signal representation. An example is provided in “A Window Switching Algorithm for AVS Audio Coding” by Shuixian Et al., NETWORKING AND MOBILE COMPUTING, WICOM 2007, INTERNATIONAL CONFERENCE ON, IEEE, PISCATAWAY, NJ, USA, September 21, 2007 ”.

In general, the (fundamental) tone of a signal will be understood as the lowest dominant frequency that is distinguished from the signal spectrum. In the common expression model, the tone is the frequency of the excitation signal modulated by the human throat. If only a simple fundamental frequency were present, the spectrum would be extremely simple, comprising the fundamental frequency and overtones only. Such a spectrum could be encoded very efficiently. For signals with variable tone, however, the energy corresponding to each harmonic component is dispersed over several transformation coefficients, thus leading to a reduction in coding efficiency.

In order to solve this reduction in coding efficiency, the audio signal to be encoded is effectively resampled on a non-uniform time grid. In subsequent processing, the sample positions obtained by non-uniform resampling are processed as if they represented the values on a uniform time grid. This operation is usually denoted by the phrase "time distortion." The sample times can be chosen advantageously depending on the temporal variation of the tone, so that a variation of tone in the distorted time version of the audio signal is smaller than a tone variant in the original version of the audio signal (before time distortion). This variant of tone can also be denoted by the phrase "time distortion contour". After the time distortion of the audio signal, the distorted time version of the audio signal becomes the frequency domain. The time-dependent distortion of the tone has the effect that the frequency domain representation of the time-distorted audio signal typically exhibits a compaction of energy in a much smaller number of spectral components than a frequency domain representation of the original audio signal (no time distortion).

On the decoder side, the frequency domain representation of the time distorted audio signal is converted back to the time domain, so that a time domain representation of the time distorted audio signal is available at The decoder side. However, in the time domain representation of the distorted audio signal in the reconstructed time on the decoder side, variations of the original tone of the input audio signal by the decoder side are not included. Consequently, another time distortion is even applied by the resampling of the reconstructed time domain representation on the decoder side of the time distorted audio signal. In order to obtain a good reconstruction of the input audio signal from the decoder side in the decoder, it is desired that the time distortion of the decoder side be at least approximately the inverse operation with respect to the side time distortion of the decoder. In order to obtain an appropriate time distortion, it is desired to have information available in the decoder which allows an adjustment of the time distortion on the decoder side.

Since it is typically required to transfer this type of information from the audio signal encoder to the Audio signal decoder, it is desired to maintain a small bit rate required for this transmission while still allowing a reliable reconstruction of the required time distortion information on the decoder side.

In view of the above description, there is a desire to create a concept which allows an efficient bit rate application of the concept of time distortion in an audio encoder.

It is the objective of some comparison examples to create concepts for improving ear impression provided by an encoded audio signal based on information available in a time distortion audio signal encoder or a distortion audio signal decoder of weather.

This objective is achieved by a provider of the time distortion activation signal to provide a time distortion activation signal based on a representation of an audio signal as described below, an audio signal encoder to encode an input audio signal as described below, a method for providing a time distortion activation signal as described below, a method for providing an encoded representation of an input audio signal such as described below, or a computer program as described below.

It is a further objective of the present invention to provide an improved audio coding / decoding scheme, which provides a lower or higher quality bit rate

This objective is obtained by means of an audio encoder according to claim 1 or 2, an audio coding method according to claim 3 or 4 or a computer program according to claim 5.

The embodiments according to the invention relate to methods for a time distortion MDCT transformation encoder. Some embodiments relate to tools only for the encoder. However, other embodiments also refer to tools for the decoder.

An embodiment according to a comparison example creates a provider of the time distortion activation signal to provide a time distortion activation signal based on a representation of an audio signal. The provider of the time distortion activation signal comprises an energy compaction information provider configured to provide an energy compaction information describing an energy compaction in a representation of the spectrum transformed by the time distortion of the signal. Audio. The provider of the time distortion activation signal also comprises a comparator configured to compare the energy compaction information with a reference value, and to provide the time distortion activation signal depending on a comparison result.

This embodiment is based on the finding that the use of a time distortion functionality in an audio signal encoder typically grants an improvement, in the sense of a reduction in the bit rate of the encoded audio signal, if the Representation of the spectrum transformed by the time distortion of the audio signal comprises a sufficiently compact energy distribution in which the energy is concentrated in one or more spectral regions (or spectral lines). This is due to the fact that a successful time distortion gives the effect of decreasing the bit rate by transforming a muddy spectrum, for example of an audio frame, within the spectrum that has one or more discernible peaks, and consequently has a compaction of energy higher than the spectrum of the original audio signal (without time distortion).

With respect to this publication, it should be understood that an audio signal frame, during which the tone of the audio signal varies significantly, comprises a muddy spectrum. The tone with variation in the time of the audio signal has the effect that a transformation of time domain in a frequency domain over the frame of the audio signal results in a muddy distribution of the signal energy over the frequency , particularly in the region with the highest frequency. Consequently, a representation of the spectrum of this original audio signal (without time distortion) comprises a low energy compaction and typically does not exhibit spectral peaks in a higher frequency portion of the spectrum, or only exhibits relatively small spectral peaks in the highest frequency portion of the spectrum. In contrast, if the time distortion is successful (in terms of providing improved coding efficiency), the time distortion of the original audio signal gives a time distorted audio signal that has a spectrum with relatively spikes. louder and clearer (particularly in the higher frequency portion of the spectrum). This is due to the fact that an audio signal with a time-varying tone is transformed into a time-distorted audio signal that has a smaller tone variation or even an approximately constant tone. Consequently, the representation of the spectrum of the audio signal distorted over time (which can be considered as a representation of the spectrum transformed by the time distortion of the audio signal) comprises one or more clear spectral peaks. In other words, the spotting of the spectrum of the original audio signal (which has a temporarily variable tone) is reduced by a successful time distortion operation, so that the representation of the spectrum transformed by the time distortion of the signal from audio comprises more energy compaction high than the spectrum of the original audio signal. However, time distortion is not always successful in improving coding efficiency. For example, time distortion does not improve coding efficiency if the input audio signal comprises large noise components, or if the time distortion contour extracted is inaccurate.

In view of this situation, the energy compaction information provided by the energy compaction information provider is a valuable indicator for deciding whether the time distortion is successful in terms of speed reduction.

One embodiment creates a provider of the time distortion activation signal to provide a time distortion activation signal based on a representation of an audio signal. The time distortion activation provider comprises two time distortion representation providers configured to provide two representations of the time distortion of the same audio signal using different time distortion contour information. In this way, the providers of time distortion representation can be configured (structurally and / or functionally) in the same way and use the same audio signal but different time distortion contour information. The time distortion activation signal provider further comprises two providers of energy compaction information configured to provide a first energy compaction information based on the first time distortion representation and provide a second energy compaction information. energy based on the second time distortion representation. The energy compaction information providers may be configured in the same way but to use the different time distortion representations. Moreover, the provider of the time distortion activation signal comprises a comparator for comparing the two different energy compaction information and providing the time distortion activation signal depending on a comparison result.

In a preferred embodiment, the energy compaction information provider is configured to provide a spectral flatness measurement that describes the representation of the spectrum transformed by the time distortion of the audio signal as the energy compaction information. It has been found that the time distortion is successful, in terms of reducing a bit rate, if you transform a spectrum of an input audio signal into a less flat time distortion spectrum representing a time-distorted version of the audio input signal Consequently, the spectral flatness measurement can be used to decide, without performing a complete spectral coding process, whether the time distortion must be activated or deactivated.

In a preferred embodiment, the energy compaction information provider is configured to compute a ratio of a geometric medium of a time distortion transformation power spectrum geometric medium and a transformation power spectrum spectrum arithmetic medium of the time distortion, to obtain the measure of spectral flatness. It has been found that this ratio is a measure of spectral flatness which is well adapted to describe the possible bit rate savings that can be obtained by a time distortion.

In another preferred embodiment, the energy compaction information provider is configured to emphasize a higher frequency portion of the transformed spectrum representation by time distortion when compared to a lower frequency portion of the transformed spectrum representation. by time distortion, to obtain the energy compaction information. This concept is based on the finding that time distortion typically has a much greater impact on the higher frequency range than on the lower frequency range. Consequently, a dominant evaluation of the higher frequency range is appropriate to determine the effectiveness of time distortion using a measure of spectral flatness. In addition, typical audio signals exhibit harmonic content (which comprises the harmony of a fundamental frequency) which decays in intensity with increasing frequency. An emphasis of a higher frequency portion of the representation of the spectrum transformed by the time distortion when compared with a lower frequency portion than the representation of the spectrum transformed by the time distortion also helps to compensate for this typical decay of the spectral lines with increasing frequency. To summarize, an emphasized consideration of the higher frequency portion of the spectrum gives increased reliability of the energy compaction information and therefore allows a more reliable provision of the time distortion trigger signal.

In another preferred embodiment, the energy compaction information provider is configured to provide a plurality of band-by-band spectral band measurements, and to compute an average of the plurality of band-by-band spectral band measurements, to obtain the information of energy compaction It has been found that the consideration of spectral measures of band-by-band flatness gives particularly reliable information as to whether the time distortion is effective in reducing the bit rate of an encoded audio signal. First, the coding of the spectrum representation transformed by the time distortion is typically performed in the band-by-band mode, so that a combination of The band-by-band measures of spectral flatness are well adapted to the coding and therefore represent an improvement that can be obtained from the bit rate with good precision. In addition, a band-by-band computation of spectral flatness measures substantially eliminates the dependence of energy compaction information from a distribution of harmony. For example, even if a higher frequency band comprises a relatively small energy (smaller than the lower frequency band energies), the higher frequency band may even be significantly relevant. However, the positive impact of a time distortion (in the sense of a reduction in spectral line staining) on this higher frequency band would be considered as low, simply due to the low energy of the higher frequency band. , If the spectral flatness measurement is not computed in the band-by-band mode. In contrast, when applying the band-by-band calculation, a positive impact of the time distortion with an appropriate weight can be taken into account, because the band-by-band spectral flatness measurements are independent of the absolute energies in the respective frequency bands .

In another preferred embodiment, the provider of the time distortion activation signal comprises a reference value calculator configured to compute a spectral flatness measurement that describes a representation of the spectrum without time distortion of the audio signal, to obtain the reference value. Accordingly, the time distortion activation signal may be provided on the basis of a comparison of the spectral flatness of a timeless distortion version of the input audio signal and a spectral flatness of a time distortion version of The input audio signal.

In another preferred embodiment, the energy compaction information provider is configured to provide a perceptual entropy measurement that describes the representation of the spectrum transformed by the time distortion of the audio signal as the energy compaction information. This concept is based on the finding that the perceptual entropy of the representation of the spectrum transformed by the time distortion is a good estimate of a number of bits (or a speed) required to encode the spectrum transformed by the time distortion. Consequently, the measure of perceptual entropy of the representation of the spectrum transformed by time distortion is a good measure of whether a reduction in bit rate can be expected by time distortion, even in view of the fact that information from Additional time distortion must be encoded if time distortion is used.

In another preferred embodiment, the energy compaction information provider is configured to provide an autocorrelation measure that describes an autocorrelation of a representation of the time distortion of the audio signal as the energy compaction information. This concept is based on the finding that the efficiency of time distortion (in terms of bit rate reduction) can be measured (or at least estimated) on the basis of a time domain signal (or a resampled not uniform) distorted over time. The time distortion has been found to be efficient if the time domain signal distorted in time comprises a relatively high degree of periodicity, which is reflected by the autocorrelation measurement. In contrast, if the time domain signal distorted in time does not comprise a significant periodicity, it can be concluded that the time distortion is not efficient.

This finding is based on the fact that an efficient time distortion transforms a portion of a sinusoidal signal of a variable frequency (which does not comprise a periodicity) into a portion of a sinusoid signal of approximately constant frequency (which comprises a degree high periodicity). In contrast, if the time distortion is not able to provide a time domain signal with a high degree of periodicity, it can be expected that the time distortion does not provide significant bit rate savings, which would justify its application.

In a preferred embodiment, the energy compaction information provider is configured to determine a sum of absolute values of a normalized autocorrelation function (over a plurality of delay values) of the time distortion representation of the audio signal, to obtain the energy compaction information. It has been found that the determination of the complex by computation of autocorrelation peaks is not necessary to estimate the efficiency of time distortion. Instead, it has been found that an evaluation of the sum of the autocorrelation over a (wide) range of autocorrelation delay values also provides very reliable results. This is due to the fact that time distortion actually transforms a plurality of signal components (for example a fundamental and harmonic frequency thereof) of variable frequency into periodic signal components. Consequently, autocorrelation of this type time distortion signal exhibits peaks at a plurality of autocorrelation delay values. Thus, sum formation is a computationally efficient way to extract energy compaction information from the autocorrelation.

In another preferred embodiment, the provider of the time distortion activation signal comprises a reference value calculator configured to compute the reference value on the basis of a spectral representation without time distortion of the audio signal or on the basis of a time domain representation without time distortion of the audio signal. In this case, the comparator is typically configured to form a relationship value using the energy compaction information describing an energy compaction in a spectrum transformed by the time distortion of the audio signal and the reference value. The comparator is also configured to compare the relationship value with one or more threshold values to obtain the time distortion activation signal. It has been found that the relationship between an energy compaction information in the case without time distortion and the energy compaction information in the case of time distortion allows efficient generation from the computational point of view at the same time sufficiently Reliable time distortion activation signal.

Another preferred embodiment creates an audio signal encoder to encode an input audio signal, to obtain an encoded representation of the input audio signal. The audio signal encoder comprises a time distortion transformer configured to provide a representation of the spectrum transformed by the time distortion based on the input audio signal. The audio signal encoder further comprises a provider of the time distortion activation signal, as described above. The provider of the time distortion activation signal is configured to receive the input audio signal and provide the energy compaction information so that the energy compaction information describes an energy compaction in the representation of the spectrum transformed by Time distortion of the input audio signal. The audio signal encoder further comprises a controller configured to selectively provide, depending on the time distortion trigger signal, a portion of the non-constant time distortion contour found (variable) or time distortion information. , or a portion of constant standard (non-variable) time distortion contour or time distortion information to a time distortion transformer. In this way, it is possible to selectively accept or reject a non-constant time distortion contour portion found in the derivation of the encoded audio signal representation from the input audio signal.

This concept is based on the finding that it is not always efficient to enter a time distortion information into an encoded representation of the input audio signal, because a significant number of bits is necessary to encode the time distortion information. In addition, it has been found that the energy compaction information, which is computed by the provider of the time distortion activation signal, is a computationally efficient measure to decide whether it is advantageous to provide a power transformer. Time distortion with the portion of time distortion contour found variable (non-constant) or a standard time distortion contour (non-variable, constant). It should be noted that when a time distortion transformer comprises an overlapping transform, a portion of the time distortion contour found can be used in the computation of two or more subsequent transform blocks. In particular, it has been found that it is not necessary to fully encode both the version of the spectral representation transformed by the time distortion of the input audio signal using the newly found time distortion contour portion variable and the version of the representation spectral transformed by the time distortion of the input audio signal using a standard (non-variable) time distortion contour portion in order to be able to make the decision whether the time distortion allows saving in bit rate or not . On the other hand, it has been found that an evaluation of the energy compaction of the spectral representation transformed by the time distortion of the input audio signal forms a reliable basis for the decision. Consequently, a required bit rate can be kept low.

In a further preferred embodiment, the audio signal encoder comprises an output interface configured to selectively, depending on the time distortion activation signal, a time distortion contour information representing a distortion contour. found time variable within the encoded representation of the audio signal In this way, high efficiency of the audio signal encoding can be obtained, regardless of whether the input signal is well adjusted for time distortion or do not.

A further embodiment according to a comparison example creates a method for providing a time distortion activation signal based on an audio signal. The method fulfills the functionality of the provider of the time distortion activation signal and can be supplemented by any of the features and functionality described herein with respect to the provider of the time distortion activation signal.

Another embodiment according to a comparison example creates a method for encoding an input audio signal, to obtain an encoded representation of the input audio signal. This method can be supplemented by any of the features and functionalities described herein with respect to the audio signal encoder.

Another embodiment according to a comparison example creates a computer program to perform the methods mentioned herein.

According to a first aspect, an analysis of the audio signal, if an audio signal has a harmonic characteristic or an expression characteristic is advantageously used to control the processing of the noise filling of the encoder side and / or decoder side. The analysis of the audio signal can be easily obtained in a system, in which a time distortion functionality is used, since this time distortion functionality typically comprises a tone follower and / or a signal classifier to distinguish between the expression on the one hand and the music on the other and / or to distinguish between the sound expression and the deaf expression. Since this information is available in such a context without additional costs, the available information is advantageously used to control the noise fill feature so that, especially for expression signals, a noise fill between harmonic lines it is reduced, or, for particular expression signals, it is even eliminated. Even in situations, where harmonic content is obtained, by an expression it is not detected directly by the expression detector, a reduction in noise filling will however result in a higher perception quality. While this feature is particularly useful in a system, in which harmonic / expression analysis is carried out in any way, and this information, therefore, is available at no additional cost, the control of the noise fill scheme It is based on a signal analysis, if the signal has a harmonic or expression characteristic or not, it is also useful, even when a specific signal analyzer must be inserted into the system, since the quality is improved without increasing bit rate or, alternatively, the bit rate is decreased without having a loss of quality, since the bits required to encode the noise fill level are reduced when the noise fill level itself , which can be transmitted from an encoder to a decoder, is reduced.

In one aspect of the present invention, the result of the signal analysis is, if the signal is a harmonic signal or an expression signal is used to control the processing of the window function of an audio encoder. It has been found that in a situation, in which an expression signal or a harmonic signal is initiated, the possibility is high so that a simple encoder will change from long windows to short windows. These short windows, however, have a correspondingly reduced frequency resolution which, on the other hand, would decrease the encoding gain for strongly harmonic signals and therefore increase the number of bits necessary to encode this type of signal portion. In view of this, the present invention defined in this aspect uses windows longer than a short window when the start of an expression or harmonic signal is detected. Alternatively, the windows are selected with a length approximately similar to the long windows, but with a shorter overlay in order to effectively reduce the pre-echoes. In general, the signal characteristic, if the time frame of an audio signal has a harmonic or expression characteristic, is used to select a window generation function for this time frame.

According to an additional aspect, the TNS (temporal noise format) tool is controlled based on whether the underlying signal is based on a time distortion function or is in a linear domain. Typically, a signal that has been processed by a time distortion function will have a strong harmonic content. Otherwise, a tone follower associated with a time distortion stage will not output a valid tone contour and, in the absence of this valid tone contour, a time distortion functionality would have been deactivated for this time frame. of the audio signal. However, harmonic signals will normally be suitable for TNS processing. TNS processing is particularly useful and induces a significant gain in bit rate / quality, when the signal processed by the TNS stage has a fairly flat spectrum. However, when the appearance of the signal is tonal, that is, not flat, as is the case of spectra that have harmonic content or sound content, the gain in quality / bit rate provided by the TNS tool will be reduced. Therefore, without modification of the TNS tool, time-distorted portions would typically not be processed by the TNS, but would be processed without a TNS filter. On the other hand, the TNS noise formatting feature nonetheless provides improved quality specifically in situations, where the signal is variable in amplitude / power. In cases, where a start of a harmonic signal or expression signal is present, and where the block change feature is implemented so that, instead of this start, long windows or at least windows longer than short windows are maintained, the activation of the temporal noise format feature for this frame will result in a noise concentration around the start of expression which effectively reduces the pre-echoes, which could occur before the start of the expression due to quantification of the frame that occurs in the processing of the subsequent encoder.

According to an additional aspect, a variable number of lines is processed by an entropic quantizer / encoder within an audio coding apparatus, in order to account for the variable bandwidth, which is introduced from frame to frame due to the embodiment of the time distortion function with a variable time distortion / contour distortion feature. When the time distortion function results in the situation that the frame time (in linear terms) included in a time distortion frame is increased, the bandwidth of a single frequency line is decreased, and, for a Constant full bandwidth, the number of frequency lines to be processed must be increased with respect to a situation without time distortion. When, on the other hand, the time distortion function results in the fact that the real time of the audio signal in the time distortion domain decreases with respect to the block length of the audio signal in the domain linearly, the frequency bandwidth of a single frequency line is increased and, therefore, the number of lines processed by a source encoder must be increased with with respect to a situation without time distortion in order to have a reduced bandwidth variation or, optimally, without bandwidth variation.

Preferred embodiments are described below with respect to the accompanying drawings, in which:

Figure 1 shows a schematic block diagram of a provider of the time distortion activation signal, according to an embodiment according to a comparison example;

Figure 2a shows a schematic block diagram of an audio signal encoder, according to an embodiment according to a comparison example;

Figure 2b shows another schematic block diagram of a provider of the time distortion activation signal according to a comparison example;

Figure 3a shows a graphical representation of a spectrum of a version without time distortion of an audio signal;

Figure 3b shows a graphical representation of a spectrum of a time distortion version of the audio signal;

Figure 3c shows a graphical representation of an individual calculation of spectral flatness measurements for different frequency bands;

Figure 3d shows a graphical representation of a calculation of a measure of spectral flatness that considers only the highest frequency portion of the spectrum;

Figure 3e shows a graphical representation of a calculation of a spectral planarity measure using a spectral representation in which a higher frequency portion is emphasized over a lower frequency portion;

Figure 3f shows a schematic block diagram of an energy compaction information provider, according to another embodiment according to a comparison example;

Figure 3g shows a graphic representation of an audio signal that has a temporarily variable tone in the time domain;

Figure 3h shows a graphical representation of a time distortion version (resampled non-uniform) of the audio signal of Figure 3g;

Figure 3i shows a graphic representation of an autocorrelation function of the audio signal according to Figure 3g;

Figure 3j shows a graphic representation of an autocorrelation function of the audio signal according to Figure 3h;

Figure 3k shows a schematic block diagram of an energy compaction information provider, according to another embodiment according to a comparison example;

Figure 4a shows a flow chart of a method for providing a time distortion activation signal based on an audio signal;

Figure 4b shows a flow chart of a method for encoding an input audio signal to obtain an encoded representation of the input audio signal, according to an embodiment according to a comparison example;

Figure 5a illustrates a preferred embodiment of an audio encoder having aspects of the invention;

Figure 5b illustrates a preferred embodiment of an audio decoder having aspects of the invention;

Figure 6a illustrates a preferred embodiment of the noise fill aspect according to a comparison example; Figure 6b illustrates a table defining the control function performed by the noise fill level manipulator;

Figure 7a illustrates a preferred embodiment for carrying out a block change based on time distortion according to the present invention;

Figure 7b illustrates an alternative embodiment to influence the window generation function;

Figure 7c illustrates a further alternative embodiment to illustrate the window generation function based on the time distortion information;

Figure 7d illustrates a window generation sequence of normal AAC behavior at a sound start;

Figure 7e illustrates alternative window sequences according to a preferred embodiment of the present invention; Figure 8a illustrates the preferred embodiment of a control based on the time distortion of the TNS tool (temporal noise format) according to a comparison example;

Figure 8b illustrates a table defining the control procedures performed in the threshold control signal generator in Figure 8a;

Figure 9a-9e illustrates different time distortion characteristics and the corresponding influence on the bandwidth of the audio signal that is produced following a time regeneration function on the decoder side;

Figure 10a illustrates a preferred embodiment of a controller for controlling the number of lines within an encoding processor according to a comparison example;

Figure 10b illustrates the dependence between the number of lines to be discarded / added for a sampling rate;

Figure 11 illustrates a comparison between a linear time scale and a distorted time scale;

Figure 12a illustrates an implementation in the context of bandwidth extension; Y

Figure 12b illustrates a table showing the dependence between the local sampling rate in the time distortion domain and the control of spectral coefficients.

Figure 1 shows a schematic block diagram of the provider of the time distortion activation signal, according to an embodiment of the invention. The provider 100 of the time distortion activation signal is configured to receive a representation 110 of an audio signal and provide, on the basis thereof, a time distortion activation signal 112. The provider 100 of the time distortion activation signal comprises a provider 120 of energy compaction information, which is configured to provide an energy compaction information 122, which describes an energy compaction in a representation of the spectrum transformed by Time distortion of the audio signal. The provider 100 of the time distortion activation signal further comprises a comparator 130 configured to compare the energy compaction information 122 with a reference value 132, and to provide the time distortion activation signal 112 depending on the result of the comparison.

As mentioned earlier, it has been found that the energy compaction information is valuable information which allows whether a time distortion grants bit storage or not. It has been found that the presence of bit storage is closely correlated with the question whether the time distortion results in a power compaction or not.

Figure 2a shows a schematic block diagram of an audio signal encoder 200, according to an embodiment of the invention. The audio signal encoder 200 is configured to receive an input audio signal 210 (also referred to as a (t)) and provide, on the basis thereof, an encoded representation 212 of the input audio signal 210. The audio signal encoder 200 comprises a time distortion transformer 220, which is configured to receive the input audio signal 210 (which may be represented in a time domain) and provide, on the basis thereof, a spectral representation 222 transformed by the time distortion of the input audio signal 210. The audio signal encoder 200 further comprises a time distortion analyzer 284, which is configured to analyze the input audio signal 210 and provide, on the basis thereof, information 286 of the time distortion contour (by example absolute or relative information of the time distortion contour).

The audio signal encoder 200 further comprises a switching mechanism, for example in the form of a controlled change 240, to decide whether the information 286 of the time distortion contour found or a standard information 288 of the time distortion contour is used for other processing. Thus, the switching mechanism 240 is configured to provide selectively, depending on a time distortion activation information, either the information 286 found from the time distortion contour or a standard information 288 from the distortion contour of time as new information of the time distortion contour 242, for further processing, for example for a time distortion transformer 220. It should be noted that a time distortion transformer 220 can use for example the new information 242 of the time distortion contour (for example a new portion of time distortion contour) and, in addition, a time distortion information previously obtained (for example one or more previously obtained time distortion contour portions) for the time distortion of an audio frame. The post-processing of the optional spectrum may comprise for example a temporary noise format and / or a noise fill analysis. The audio signal encoder 200 further comprises a quantifier / encoder 260, which is configured to receive the spectral representation 222 (optionally processed by post processing 250 of the spectrum) and to quantify and encode the transformed spectral representation 222. To this end , the quantifier / encoder 260 can be coupled with a perceptual model 270 and receive information 272 of perceptual relevance from the perceptual model 270, to consider a perceptual masking and adjust the quantification accuracies in different frequency boxes according to human perception. The audio signal encoder 200 further comprises an output interface 280 which is configured to provide the encoded representation 212 of the audio signal based on the quantified and encoded representation 262 supplied by the quantizer / encoder 260.

The audio signal encoder 200 further comprises a provider of the time distortion activation signal 230, which is configured to provide a time distortion activation signal 232. The time distortion activation signal 232, for example, can be used to control the switching mechanism 240, to decide whether the information 286 of the newly found time distortion contour or a standard information 288 of the time distortion contour it is used in other processing steps (for example by a time distortion transformer 220). In addition, time distortion activation information 232 can be used on a contact 280 to decide whether information 242 of the newly selected time distortion contour (selected from information 286 of the newly found time distortion contour and standard information of the time distortion contour) is included in the encoded representation 212 of the input audio signal 210. Typically, the time distortion contour information is only included in the encoded representation 212 of the audio signal if the selected time distortion contour information describes a non-constant (variable) time distortion contour. In addition, the information 232 of the activation of the time distortion may itself be included in the encoded representation 212, for example in the form of a one-bit mark indicating the activation or deactivation of the time distortion.

In order to facilitate understanding, it should be noted that a time distortion transformer 220 typically comprises a window generator (window generator) for analysis 220a, a resampler or "time distorter" 220b ( time warper) and a domain transformer spectral (or time / frequency converter) 220c. Depending on the implementation, however, the time distorter 220b can be placed - in a signal processing direction - before the analysis window generator 220a. However, time distortion and time domain for spectral domain transformation can be combined into a single unit in some embodiments.

Details regarding the operation of the provider of the time distortion activation signal 230 will be described below. It should be noted that the provider of the time distortion activation signal 230 may be equivalent to the supplier 100 of the activation signal of time distortion

The provider 230 of the time distortion activation signal is preferably configured to receive representation 210 of the audio signal of the time domain (further designated with a (t)), information 286 of the time distortion contour recently found, and the 288 standard information of the time distortion contour. The provider 230 of the time distortion activation signal is further configured to obtain, using the time domain audio signal 210, the newly found time distortion contour information 286 and the standard distortion contour information 288 time, an energy compaction information describing an energy compaction due to the information 286 of the recently found time distortion contour, and providing the time distortion activation signal 232 based on this energy compaction information.

Figure 2b shows a block schematic diagram of a provider 234 of the time distortion activation signal, according to an embodiment of the invention. The provider 234 of the time distortion activation signal may take the role of the supplier 230 of the time distortion activation signal in some embodiments. The provider 234 of the time distortion activation signal is configured to receive an input audio signal 210, and two information 286 and 288 of the time distortion contour, and provide, on the basis thereof, a 234p time distortion activation signal. The time distortion activation signal 234p can take the function of the time distortion activation signal 232. The provider of the time distortion activation signal comprises two providers 234a, 234g of the identical time distortion representation, which are configured to receive the input audio signal 210 and the information 286 and 288 of the distortion contour of time respectively and provide, on the basis thereof, two representations 234e and 234k of time distortion, respectively. The provider 234 of the time distortion activation signal further comprises two providers 234f and 234l of identical energy compaction information, which are configured to receive the time distortion representations 234e and 234k, respectively, and, based on thereof, provide the 234m and 234n energy compaction information, respectively. The provider of the time distortion activation signal further comprises a comparator 234o, configured to receive the energy compaction 234m and 234n information, and, on the basis thereof, provide the time distortion activation signal 234p.

In order to facilitate understanding, it should be noted that providers 234a and 234g of time distortion representation typically comprise (optional) identical generators 234b and 234h of analysis windows, identical resamplers or time distorters 234c and 234i, and (optional ) identical transformers 234d and 234j of the spectral domain.

Next, different concepts for obtaining energy compaction information will be mentioned. In advance, an introduction will be given explaining the effect of time distortion on a typical audio signal. Next, the effect of time distortion on an audio signal will be described with reference to Figures 3a and 3b. Figure 3a shows a graphical representation of a spectrum of an audio signal. An abscissa 301 describes a frequency and an ordinate 302 describes the intensity of the audio signal. A curve 303 describes the intensity of the audio signal without time distortion as a function of the frequency f.

Figure 3b shows a graphical representation of a spectrum of a time distortion version of the audio signal represented in Figure 3a. Again, an abscissa 306 describes a frequency and an ordinate 307 describes the intensity of the distorted version of the audio signal. A curve 308 describes the intensity of the time distortion version of the audio signal over the frequency. As can be seen from a comparison of the graphic representation of Figures 3a and 3b, the time-distorted version of the audio signal comprises a muddy spectrum, in particular in a higher frequency region. In contrast, the time distortion version of the input audio signal comprises a spectrum that has clearly distinguishable spectral peaks, even in the higher frequency region. In addition, moderate sharpening of the spectral peaks can even be observed in the lowest spectral region of the time distortion version of the input audio signal.

It should be noted that the spectrum of the time distortion version of the input audio signal, which is shown in Figure 3b, can be quantified and encoded, for example by quantizer / encoder 260, with a bit rate more Low than the spectrum of the input audio signal without distorting in Figure 3a. This is due to the fact that a muddy spectrum typically comprises a large number of significantly relevant spectral coefficients (ie a relatively small number of spectral coefficients quantified at zero or quantified at small values), while a "less flat" spectrum as Sample in Figure 3 typically comprises a greater number of spectral coefficients quantified at zero or quantified at small values. Spectral coefficients quantified at zero or quantified at small values can be encoded with fewer bits than spectral coefficients quantified at higher values, so that the spectrum of Figure 3b can be encoded using fewer bits than the spectrum of Figure 3a. However, it should also be noted that the use of a time distortion does not always result in a significant improvement in the coding efficiency of the time distortion signal. Consequently, in some cases, the price, in terms of bit rate, required for the encoding of the time distortion information (eg time distortion contour) may exceed the saved ones, in terms of bit rate, for encode the transformed spectrum by time distortion (when compared to the transformed spectrum without time distortion). In this case, it is preferable to provide the encoded representation of the audio signal using a standard (non-variable) time distortion contour to control the transformation of the time distortion. Consequently, the transmission of any time distortion information (i.e. time distortion contour information) can be omitted (except for a mark indicating the deactivation of time distortion), thus maintaining the bit rate. low.

Next, different concepts for a reliable and efficient computational calculation of a signal 112 232, 234p of time distortion activation, will be described by reference to Figures 3c-3k. However, before that, the background of the concept of the invention will be briefly summarized.

The basic assumption is that the application of time distortion on a harmonic signal with a variable tone makes the constant tone, and by making the constant tone improves the coding of the spectra obtained by a subsequent transformation of the time frequency, because instead of staining the different harmonies on several spectral boxes (see figures 3a) only one number Limited significant lines remain (see Figure 3b). However, even when a tone variant is detected, the improvement in encoding gain (i.e. the amount of stored bits) can be negligible (for example if one has a loud noise underlying the harmonic signal, or if the variant it is so small that spotting the higher harmonies is not a problem), or it may be less than the amount of bits needed to transfer the time distortion contour to the decoder, or it may simply be incorrect. In these cases, it is preferable to reject the variable time distortion contour (for example 286) produced by a time distortion contour encoder and instead use efficient one bit signaling, signaling a standard time distortion contour ( not variable).

The scope of the present invention comprises the creation of a method for deciding whether a time distortion contour portion obtained provides sufficient coding gain (for example sufficient coding gain to compensate for the unused free space required for coding for the contour of time distortion).

As stated earlier, the most important aspect of time distortion is the compaction of the spectral energy for a smaller number of lines (see Figures 3a and 3b). A look at this shows that a compaction of energy also corresponds to a spectrum more "without flatness" (see Figures 3a and 3b), since the difference between the peaks and valleys of the spectrum is increased. The energy is concentrated in less lines having the lines between those less energy than before.

Figures 3a and 3b show a schematic example with a distortion-free spectrum of a frame with strong harmonies and tone variation (figure 3a) and the time distortion version of the same frame (figure 3b).

In view of this situation, it has been found that it is advantageous to use the spectral flatness measurement as a possible measure for the efficiency of time distortion.

Spectral flatness can be calculated, for example, by dividing the geometric medium of the power spectrum by the arithmetic means of the power spectrum. For example, spectral flatness (also briefly designated as "flatness") can be computed according to the following equation:

Flatness

In the above, x (n) represents the magnitude of a binary number n. In addition, in the above, N represents a total number of spectral boxes considered for the calculation of the spectral flatness measurement.

In one embodiment of the invention, the above-mentioned calculation of "flatness", which may be useful as an energy compaction information, can be carried out using representations 234e, 234k of the spectrum transformed by time distortion, so that the following relationship can be sustained:

x (n) = | x | tw (n).

In this case, N can be equal to the number of spectral lines provided by the spectral domain transformer 234d, 234j and | x | tw (n) is a representation 234e, 234k of the transformed time distortion spectrum.

Although the spectral measurement is a useful amount for the provision of the time distortion activation signal, a disadvantage of the spectral flatness measurement, as well as the signal to noise ratio (SNR) measurement, is that if applied to the Total spectrum, emphasizes parts with higher energy. Normally, harmonic spectra have a certain spectral inclination, which means that most of the energy is concentrated in the first few partial tones and then decreases with increasing frequency, leading to a sub-representation of the higher partials in the measure. This is not desired in some embodiments, since it is the intention to improve the quality of these higher partials, because they get muddy most of the time (see Figure 3a). Next, several optional concepts for improving the relevance of the spectral flatness measurement will be described.

In an embodiment according to the invention, an approach similar to the so-called "segmental SNR" measure is chosen, which leads to a measure of the spectral flatness band by band. A calculation of the flatness measure Spectral is performed (for example separately) within a number of bands, and the main (or middle) is taken. Different bands could have equal bandwidth. However, preferably, the bandwidths can follow a perceptual scale, like the critical bands, or correspond, for example, to the bands of the scale factor of the so-called "advanced audio coding" also known as AAC.

The aforementioned concept will be briefly explained below, taking as reference figure 3c, which shows a graphical representation of an individual calculation of spectral flatness measurements for different frequency bands. As can be seen, the spectrum can be divided into different frequency bands 311, 312, 313, which can have an equal bandwidth or which can have different bandwidths. For example, a first measure of spectral flatness can be computed for the first frequency band 311, for example, using the equation for the "flatness" given above. In this calculation, the frequency boxes of the first frequency band can be considered (n variable in functions can take the frequency box indices of the frequency boxes of the first frequency band), and the width of the frequency can be considered first frequency band 311 (variable N can take the width in terms of frequency boxes of the first frequency band). Consequently, a planarity measure is obtained for the first frequency band 311. Similarly, a measure of flatness can be computed for the second frequency band 312, taking into account the frequency boxes of the second frequency bands 312 and also the width of the second frequency band. Moreover, the flatness measures of the additional frequency bands, of the third frequency band type 313, can be computed in the same way.

Next, an average of the fullness measurements for different frequency bands 311, 312, 313 can be computed and the average can be useful as the energy compaction information.

Another approach (for improving the derivation of the time distortion activation signal) is to apply the spectral planarity measurement only above a certain frequency. This approach is illustrated in Figure 3b. As can be seen, only frequency boxes in a higher frequency portion 316 of the spectra are considered for a calculation of the spectral planarity measurement. A lower frequency portion of the spectrum is insignificant for the calculation of the spectral flatness measurement. The higher frequency portion 316 can be considered frequency band by frequency band for the calculation of the spectral flatness measurement. Alternatively, the full higher frequency portion 316 may be considered in its entirety for the calculation of the spectral flatness measurement.

To summarize the above, it can be established that the decrease in spectral flatness (caused by the application of time distortion) can be considered as a first measure for the efficiency of time distortion.

For example, the provider 100, 230, 234 of the time distortion activation signal (or comparator 130, 234 ° thereof) can compare the spectral planarity measurement of the spectral representation 234e transformed by the time distortion with a Spectral planarity measurement of the spectral representation 234k transformed by the time distortion using a standard time distortion contour information, and to decide on the basis of such comparison whether the time distortion trigger signal must be active or inactive. For example, the time distortion is activated by an appropriate configuration of the time distortion trigger signal if the time distortion results in a sufficient reduction of the spectral planarity measurement when compared to a case without distortion of weather.

In addition to the approaches mentioned above, the upper frequency portion of the spectrum can be emphasized (for example by appropriate scaling) on the lower frequency portion for the calculation of the spectral flatness measurement. Figure 3c shows a graphical representation of a spectrum transformed by the time distortion in which a higher frequency portion is emphasized over a lower frequency portion. Consequently, a higher sub-representation of partials in the spectrum is compensated. Thus, the flatness measurement can be computed on the full scaled spectrum in which the higher frequency boxes are emphasized on the lower frequency boxes, as shown in Figure 3e.

As for bit storage, a typical measure to encode efficiency would be perceptual entropy, which can be defined in a way that correlates very well with the actual number of bits needed to encode a certain spectrum as described in 3GPP TS 26.403 V7.0.0: 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; General audio codec audio processing functions; Enhanced aacPlus general audio codec; Encoder specification AAC part: Section 5.6.1.1.3 Relation between bit demand and perceptual entropy. As a result, the reduction of perceptual entropy is another measure to determine how the efficiency of time distortion would be.

Figure 3f shows a provider 325 of energy compaction information, which can take the place of provider 120, 234f, 234l of energy compaction information, and which can be used in suppliers 100, 290, 234 of the time distortion activation signal. The 325 compaction information provider of energy is configured to receive a representation of the audio signal, for example, in the form of a representation 234e, 234k of the spectrum transformed by the time distortion, also designated with | x | tw. The energy compaction information provider 325 is further configured to provide perceptual entropy information 326, which can take the place of the energy compaction information 122, 234m, 234n.

The energy compaction information provider 325 comprises a form factor calculator 327, which is configured to receive the representation 234e, 234k of the spectrum transformed by the time distortion and provide, on the basis thereof, an information 328 of the form factor, which may be associated with a frequency band. The energy compaction information provider 325 further comprises a frequency band energy calculator 329, which is configured to calculate a frequency band energy information (330) in (n) based on the representation 234e, 234k of the time distortion spectrum. The energy compaction information provider 325 further comprises an estimator 331 of the number of lines, which is configured to provide an estimated information (332) of the number of lines nl for a frequency band having index n. In addition, the energy compaction information provider 325 comprises a perceptual entropy calculator 333, which is configured to compute the perceptual entropy information 326 based on the frequency band energy information 330 and the information 332 Estimated number of lines. For example, the form factor calculator 327 may be configured to compute the form factor according to

kOffset ^{(n 1) -1}

ffac ( n) = X 4 \ X ( k )

^{k = kOffset (n)} (1)

In the previous equation, ffac (n) designates the form factor for the frequency band that has a frequency band index n. k designates a variable in operation, which operates on the spectral box indices of the scale factor band (or frequency band) n. X (k) designates a spectral value (for example, an energy value or a magnitude value) of the spectral box (or frequency box) having a spectral box index (or a frequency box index) k.

The estimator of the number of lines can be configured to estimate the number of non-zero lines, designated with nl, according to the following equation:

In the previous equation, in (n) designate an energy in the frequency band or scale factor band n. kOffset (n + 1) -kOffset (n) designates a width of the frequency band or index scale factor band n in terms of frequency boxes.

Moreover, the perceptual entropy calculator 332 can be configured to compute the perceptual entropy information sfbPe according to the following equation:

In the above, the following relationships can be sustained:

c1 = log ² (8) c 2 = log ² (2.5) c3 = 1 - c2 / c1, (4)

A total perceptual entropy pe can be computed as the sum of the perceptual entropies of multiple frequency bands or scale factor bands.

As mentioned earlier, the perception entropy information 326 can be used as an energy compaction information.

For other details that refer to the calculation of perceptual entropy, reference is made to section 5.6.1.1.3 of the International Standard “3GPP TS 26.403 V7.0.0 (2006-06)”.

Next, a concept for the computation of the energy compaction information in the time domain will be described.

Another look at TW-MDCT (discrete cosine transform modified by time distortion) is the basic idea to change the signal in a way that has a constant or almost constant tone within a block. If a constant tone is obtained, this means that the maximum autocorrelation of a block in process increases. Since it is not trivial to find the corresponding maximum in the autocorrelation in the case of time distortion and without time distortion, the sum of the absolute values for the normalized autocorrelation can be used as a measure for improvement. An increase in this sum corresponds to an increase in energy compaction.

This concept will be explained in detail below, taking as reference the figures 3g, 3h, 3i, 3j and 3k.

Figure 3g shows a graphical representation of a signal without time distortion in the time domain. An abscissa 350 describes the time, and an ordinate 351 describes a level a (t) of the time signal without time distortion. A curve 352 describes the temporal evolution of the time signal without time distortion. It is presumed that the frequency of the signal without time distortion described by curve 352 increases in time, as can be seen in Figure 3g.

Figure 3h shows a graphical representation of a time distortion version of the time signal of Figure 3g. An abscissa 355 describes the time distortion (for example, in normalized form) and an ordinate 356 describes the level of the time distortion version a (tw) of the signal a (t). As can be seen in Figure 3h, the time distortion version a (tw) of the time signal without time distortion a (t) comprises (at least approximately) a temporarily constant frequency in the time distortion domain.

In other words, Figure 3h illustrates the fact that a time signal of a temporarily variable frequency is transformed into a time signal of a temporarily constant frequency by an appropriate time distortion operation, which may comprise a resampling of the time distortion

Figure 3i shows a graphical representation of a time signal autocorrelation function without time distortion at (t). An abscissa 360 describes a delay in autocorrelation t, and an ordinate 361 describes the magnitude of the autocorrelation function. The marks 362 describe an evolution of the autocorrelation R ^uw (t) as a function of the autocorrelation delay ^t . As can be seen from Figure 3i, the Ruw autocorrelation function of the time signal without time distortion a (t) comprises a peak for t = 0 (which reflects the energy of the signal a (t)) and takes Small values for ^t £ 0.

Figure 3j shows a graphical representation of the autocorrelation function Rtw of the time distortion time signal a (tw). As can be seen from Figure 3j, the autocorrelation function Rtw comprises a peak for t = 0, and also comprises peaks for other values t1, t2, t3 of the autocorrelation delay t. These additional peaks for t1, t2, t3 are obtained by the effect of time distortion to increase the periodicity of the time distortion time signal to (tw). This periodicity is reflected by the additional peaks of the autocorrelation function Rtw (t) when compared to the autocorrelation function R ^uw (t). Thus, the presence of additional peaks (or the increased intensity of peaks) of the autocorrelation function of the distorted audio signal over time, when compared with the autocorrelation function of the original audio signal can be used as an indication of the effectiveness (in terms of a reduction in bit rate) of time distortion.

Figure 3k shows a block schematic diagram of a provider of energy compaction information 370 configured to receive a time domain time representation of the audio signal, for example, time distortion signal 234e, 234k (where the transformed 234d, 234j of spectral domain and optionally the generator 234b and 234h of analysis windows is omitted), and provide, on the basis of it, an information 374 of energy compaction, which can take the function of 372 energy compaction information. The energy compaction information provider 370 of Figure 3k comprises an autocorrelation calculator 371 configured to compute the autocorrelation function Rtw (T) of the time distortion signal a (tw) over a predetermined range of discrete values of ^t . The energy compaction information provider 370 further comprises an autocorrelation adder 372 configured to sum a plurality of values of the autocorrelation function Rtw (T) (for example, over a predetermined range of discrete values of ^t ) and provide the sum obtained as information 122, 234m, 234n of energy compaction.

In this way, the energy compaction information provider 370 allows the provision of reliable information indicating the efficiency of time distortion without actually performing the domain transformation. spectral version of the time distortion time domain of the input audio signal 210. Therefore, it is possible to perform a spectral domain transformation of the time distortion version of the input audio signal 310 only if it is found, based on the energy compaction information 122, 234m, 234n provided by the energy compaction information provider 370, that the time distortion really gives improved coding efficiency.

To summarize the above, the embodiments according to the invention create a concept for a final quality review. A resulting tone contour (used in a time distortion audio signal encoder) is evaluated for its encoding gain and whether it is accepted or rejected. Several measures concerning the shortage of coding or spectrum gain can be taken into account for this decision, for example, a measure of spectral flatness, a measure of segmental spectral fullness band by band, and / or a perceptual entropy. The use of different spectral compaction information has been discussed, for example, the use of a measure of spectral flatness, the use of a measure of perceptual entropy, and the use of a time domain autocorrelation measure. However, there are other measures that show a compaction of energy in a spectrum distorted over time.

All these measures can be used. Preferably, for all these measurements, a relationship between the measurement for spectrum without time distortion and one with time distortion is defined, and a threshold is established for this relationship in the encoder to determine whether a time distortion contour is obtained. provides a benefit in coding or not.

All these measures can be applied to a complete frame, where only the third portion of the tone contour is new (where, for example, three portions of the tone contour are associated with the complete frame), or preferably only for the portion of the signal, for which this new portion was obtained, for example, using a transform with a low superimposed window centered on the signal portion (respective).

Naturally, a single measurement or a combination of the aforementioned measures can be used as desired.

Figure 4a shows a flow chart of a method for providing a time distortion activation signal based on an audio signal. The method 400 of Figure 4a comprises a step 410 to provide an energy compaction information describing an energy compaction in a spectral representation transformed by the time distortion of the audio signal. The method 400 further comprises a step 420 to compare the energy compaction information with a reference value. The method 400 further comprises a step 430 to deliver the time distortion activation signal depending on the result of the comparison.

The method 400 may be supplemented by any of the features and functionalities described herein with respect to the provision of the time distortion activation signal.

Figure 4b shows a flow chart of a method for encoding an input audio signal to obtain an encoded representation of the input audio signal. The method 450 optionally comprises a step 460 to provide a spectral representation transformed by the time distortion based on the input audio signal. Method 450 further comprises a step 470 to deliver a time distortion activation signal. Step 470, for example, may comprise the functionality of method 400. In this way, the energy compaction information can be provided so that the energy compaction information describes an energy compaction in the representation of the spectrum transformed by the Time distortion of the input audio signal. The method 450 further comprises a step 480 to selectively provide, with dependence on the time distortion activation signal, a description of the spectral representation transformed by the time distortion of the input audio signal using contour information of recently found time distortion or a description of a spectral representation transformed without time distortion of the input audio signal using a standard time distortion contour information (non-variable) for inclusion in the encoded representation of the signal from audio input

Method 450 may be supplemented by any of the features and functionalities mentioned herein with respect to the encoding of the input audio signal.

Figure 5 illustrates a preferred embodiment of an audio encoder according to the present invention, in which various aspects of the present invention are implemented. An audio signal is provided at an input of the encoder 500. This audio signal will typically be a discrete audio signal which has been derived from an analog audio signal using a sampling rate which is also called the normal sampling rate. . This normal sampling rate is different from a local sampling rate generated by a operation of the time distortion function, and the normal sampling rate of the audio signal at input 500 is a constant sampling rate that results in audio samples separated by a constant portion of time. The signal is placed in an analysis window generator 502, which, in this embodiment, is connected to a window generation function controller 504. The analysis window generator 502 is connected to a time warper 506 ). Depending on the implementation, however, the time distorter 506 may be placed - in a signal processing direction - before the generator 502 of analysis windows. This implementation is preferred, when a time distortion characteristic is required for the generation of block analysis windows 502, and when the time distortion function must be performed on time distortion samples instead of samples without time distortion. Specifically in the context of time distortion based on MDCT as described in Bernd Edler et al., "Time Warped MDCT", International Patent Application PCT / EP2009 / 002118. For other time distortion applications such as those described in L. Villemoes, "Time Warped Transform Coding of Audio Signals", International Patent Application PCT / EP2006 / 010246, November 2005., the placement between the 506 distorter of Time and analysis window generator 502 can be set as required. In addition, a time / frequency converter 508 is provided to perform a time / frequency conversion of a time distorted audio signal into a spectral representation. The spectral representation can be entered in a step 510 of TNS (temporal noise format), which provides, as a 5l0a output, TNS information and, as an output 510b, spectral residual values. Output 510b is coupled to a block quantifier and encoder 512 which can be controlled by a perceptual model 514 to quantify a signal so that the quantization noise is hidden below the perceptual masking threshold of the audio signal.

Additionally, the encoder illustrated in Figure 5a comprises a time distortion analyzer 516, which can be implemented as a tone follower, which provides time distortion information at output 518. The signal on line 518 can comprise a time distortion characteristic, a tone characteristic, a tone contour, or an information, if the signal analyzed by the time distortion analyzer is a harmonic signal or a signal without harmony. The time distortion analyzer can also implement the functionality to distinguish between sound expression and deaf expression. However, depending on the implementation, and if a signal classifier 520 is implemented, the sound / deaf decision can also be made by the signal classifier 520. In this case, the time distortion analyzer must not necessarily perform the same functionality. The output 518 of the time distortion analyzer is connected to at least one and preferably more than one functionality in the functionality group comprising the controller 504 of the window generating function, the time distorter 506, the step 510 of TNS, quantifier 512 and encoder and an output interface 522.

Similarly, an output 522 of the signal classifier 520 may be connected to one or more of the functionalities of a group of functionalities comprising the controller 504 of the window generation function, step 510 of TNS, an analyzer 524 of Noise padding or 522 output interface. In addition, an output 518 of the time distortion analyzer may be connected to the noise fill analyzer 524.

Although Figure 5a illustrates a situation, where the audio signal at the input 500 of the analysis window generator is input into a time distortion analyzer 516 and the signal classifier 520, the input signals for these functionalities can also be take from the output of the generator 502 of analysis windows and, with respect to the signal classifier, they can even be taken from the output of the time distorter 506, the output of the time / frequency converter 508 or the output of step 510 of TNS

In addition to the output of a signal by the encoder quantifier 512 indicated in 526, the output interface 522 receives information 510a from the side of the TNS, information 528 from the side of the perceptual model, which may include scaling factors in form encoded, time distortion indication data for more information on the side of the advanced time distortion such as the tone contour on line 518 and the signal classification information on line 522. In addition, the fill-in analyzer 524 Noise can also produce noise filler data at output 530 at output interface 522. The output interface 522 is configured to generate audio output data encoded on line 532 for transmission to a decoder or to store in a storage device such as a memory device. Depending on the implementation, the output data 532 may include all of the input on the output interface 522 or may contain less information, provided that the information is not required by the corresponding decoder, which has reduced functionality, or always that the information is already available in the decoder due to a transmission through a different transmission channel.

The encoder illustrated in Figure 5a can be implemented as defined in detail in the MPEG-4 standard in addition to the additional functionalities illustrated in the encoder of the invention in Figure 5a represented by the controller 504 of the window generation function. , noise filler analyzer 524, encoder quantifier 512 and step 510 of TNS, which have, in comparison to the MPEG-4 standard, advanced functionality. Another description is found in the AAC standard (international standard 13818-7) or 3GPP TS 26.403 V7.0.0: Third generation partnership project; technical specification group services and system aspect; general audio codec audio processing functions; enhanced AAC plus general audio codec.

Next, Figure 5b is described, which illustrates a preferred embodiment of an audio decoder for decoding an encoded audio signal received through input 540. The input interface 540 is operative to process the encoded audio signal of so that the different information information items are extracted from the signal on line 540. This information includes signal classification information 541, time distortion information 542, noise fill data 543, scale factors 544, data 545 of TNS and 546 encoded spectral information. The encoded spectral information is entered into an entropic decoder 547, which may comprise a Huffman decoder or an arithmetic decoder, provided that the functionality of the encoder in block 512 in Figure 5a is implemented in the form of the corresponding encoder such as an encoder Huffman or an arithmetic encoder. The encoded spectral information is entered into re-quantizer 550, which is connected to a noise fill 552. The output of the noise fill 552 is entered into a reverse TNS stage 554, which also receives the TNS data on line 545. Depending on the implementation, the noise fill 552 and the TNS step 554 can be applied in different order so that the noise fill 552 operates in the output data of step 554 of TNS rather than in the input data of TNS. In addition, a frequency / time converter 556 is provided, which feeds a time regenerator 558 ( time dewarper). At the output of the signal processing chain, a synthesis window generator that preferably performs an overlay / aggregate processing is applied as indicated in 560. The order of the time regenerator 558 and the synthesis step 560 can be change, but, in the preferred embodiment, it is preferred to perform an encoding / decoding algorithm based on MDCT as defined in the AAC standard (AAC = advanced audio coding). Next, the inherent cross fade operation from one block to the next due to the overlay / aggregate procedure is advantageously used as the last operation in the processing chains so that all artifacts that block the distortion in the conversion are avoided in effective way.

Additionally, a noise 562 fill analyzer is provided, which is configured to control the noise fill 552 and which receives as input, information 542 on temporal distortion and / or information on the classification 541 of signals and spectrum information. quantified, as the case may be.

Preferably, all the functionalities described hereafter are applied together in an improved audio encoder / decoder scheme. However, the functionalities described hereafter can also be applied independently to each other, that is, so that only one or a group is implemented, but not all the functionalities in a certain encoder / decoder scheme.

Subsequently, the noise fill aspect of the present invention is described in detail.

In one embodiment, the additional information provided by the tone contour / time distortion tool 516 in Figure 5a is beneficially used to control other encoder / decoder tools and, specifically, the noise fill tool implemented by the analyzer 524 noise fill of the encoder side and / or implemented by the noise fill analyzer 562 and the noise fill 552 of the decoder side.

Several encoder tools within the AAC framework such as a noise fill tool are controlled by the information collected by the analysis of tone contours and / or by additional knowledge of a signal classification provided by the signal classifier 520.

A contour of tones found indicates segments of signals with a clear harmonic structure, so that the noise filling between the harmonic lines could decrease the perceived quality, especially in expression signals, therefore the noise level is reduced, when A tone contour is found. Otherwise, there would be noise between the partial tones, which have the same effect as the increased quantization noise for a spotted spectrum. In addition, the amount of noise level reduction can also be improved using the information of the signal classifier, so that p. For example, for expression signals there would be no noise fill and a moderate noise fill would be applied to generic signals with a strong harmonic structure.

Generally, noise fill 552 is useful for inserting spectral lines into a decoded spectrum, where zeros have been transmitted from an encoder to a decoder, that is, where quantizer 512 in Figure 5a has quantified spectral lines at zero. Naturally, quantifying the spectral lines at zero greatly reduced the bit rate of the transmitted signal, and, in theory, the elimination of these (small) spectral lines is not audible, when these spectral lines are below the perceptual masking threshold according to as determined by the 514 perceptual model. However, it has been discovered that these "spectral holes", which may include many adjacent spectral lines, result in a rather unnatural sound. Therefore, a noise fill tool is provided to insert spectral lines into positions, where the lines have been quantified at zero by a quantifier on the encoder side. These spectral lines they can have a random amplitude or phase, and these spectral lines synthesized from the decoder side are scaled using a noise fill measurement determined from the encoder side as illustrated in Figure 5a or depending on a given measurement of the decoder side as illustrated in Figure 5b by optional block 562. The noise fill analyzer 524 in Figure 5a, therefore, is configured to estimate a noise fill measurement of an energy of zero-quantized audio values for a time frame of the audio signal.

In one embodiment of the present invention, the audio encoder for encoding an in-line audio signal 500 comprises quantizer 512 that is configured to quantify audio values, where quantizer 512 is also configured to quantify audio values below zero. of a quantification threshold. This quantification threshold may be the first step of a step-based quantifier, which is used for the decision, if a certain audio value is quantified at zero, that is, at a quantization index of zero, or quantified by one , that is, a quantification index of one that indicates that the audio value is above this first threshold. Although the quantifier in Figure 5a is illustrated as performing the quantification of frequency domain values, the quantifier can also be used to quantify time domain values in an alternative embodiment, in which the noise filling is performed in the time domain instead of frequency domain.

The noise fill analyzer 524 is implemented as a noise fill calculator to estimate a noise fill measurement of an energy of zero-quantized audio values for a time frame of the audio signal by the quantizer 512. Additionally, the audio encoder comprises an audio signal analyzer 600 illustrated in Figure 6a, which is configured to analyze, if the time frame of the audio signal has a harmonic characteristic or an expression characteristic. The signal analyzer 600 may, for example, comprise block 516 of Figure 5a or block 520 of Figure 5a or it may comprise any of the devices for analyzing whether a signal is a harmonic signal or an expression signal. Because the temporal distortion analyzer 516 is implemented to always look for a tone contour, and because the presence of a tone contour indicates a harmonic structure of the signal, the signal analyzer 600 in Figure 6a can be implemented as a tone tracker or a temporal distortion contour calculator of a temporal distortion analyzer.

The audio encoder additionally comprises a noise level 602 manipulator illustrated in Figure 6a, which produces a measurement / level of noise level to be produced at the output interface 522 indicated in 530 in Figure 5a. The noise fill measurement manipulator 602 is configured to manipulate the noise fill measurement depending on the harmonic or expression characteristic of the audio signal. The audio encoder additionally comprises the output interface 522 for generating an encoded signal for transmission or storage, the encoded signal comprising the manipulated noise fill measurement produced by block 602 in line 530. This value corresponds to the values produced by block 562 in the implementation of the decoder side illustrated in Figure 5b.

As indicated in Figure 5a and Figure 5b, manipulation of the noise fill level can be implemented in an encoder or can be implemented in a decoder or can be implemented in both devices together. In an implementation of the decoder side, the decoder for decoding an encoded audio signal comprises the input interface 539 for processing the encoded signal in line 540 to obtain a noise fill measurement, that is, noise fill data in line 543, audio data encoded in line 546. The decoder additionally comprises a decoder 547 and re-quantizer 550 for generating quantified data.

Additionally, the decoder comprises a signal analyzer 600 (Figure 6a) that can be implemented in the noise filler analyzer 562 in Figure 5b to retrieve information, in the case of a time frame of the audio data possesses a harmonic characteristic or expression.

Additionally, noise fill 552 is provided to generate noise fill audio data, wherein noise fill 552 is configured to generate noise fill data in response to the measurement of noise fill transmitted through the signal encoded and generated by the input interface on line 543 and the harmonic or expression characteristic of the audio data as defined by the signal analyzer 516 and / or 550 on the encoder side or as defined by item 562 on the decoder side through the processing and interpretation of the information about the temporal distortion 542 that it indicates, whether a certain time frame has undergone a temporary distortion processing or not.

Additionally, the decoder comprises a processor to process the quantified data and the noise filler audio data to obtain a decoded audio signal. The processor may include items 554, 556, 558, 560 in Figure 5b as appropriate. Additionally, depending on the specific implementation of the encoder / decoder algorithm, the processor may include other processing blocks, which are provided, for example, in a time domain encoder such as the AMR WB + encoder or others. expression encoders

The inventive noise fill manipulation can, therefore, be implemented on the encoder side only by calculating the simple noise measurement and manipulating this noise measurement based on harmonic / expression information and transmitting the manipulated noise fill measurement. already correct that then it can be applied by a decoder in a simple way. Alternatively, the unhandled noise fill measurement can be transmitted from an encoder to a decoder, and the decoder will then analyze, if the actual time frame of an audio signal has undergone a temporal distortion, that is, it has a harmonic characteristic. or expression so that the actual manipulation of the noise fill measurement occurs on the decoder side.

Subsequently, Figure 6b is discussed in order to explain the preferable embodiments for manipulating the approximate calculation of the noise level.

In the first embodiment, a normal noise level is applied, when the signal does not have a harmonic or expression characteristic. This is the case, when no temporal distortion is applied. When, in addition, a signal classifier is provided, then the signal classifier that distinguishes between expression and non-expression would indicate no expression for the situation, where temporal distortion was not active, that is, where no tone contour was found.

When, however, temporal distortion was active, that is, when a tone contour was found, indicating a harmonic content, then the level of noise fill level would be manipulated to be lower than in the normal case. When an additional signal classifier is provided, and then this signal classifier indicates expression, and when simultaneously the information on temporal distortion indicates a tone contour, then a noise fill level lower than or even equal to zero is signaled. In this way, the noise fill level manipulator 602 of Figure 6a will reduce the manipulated noise level to zero or at least to a value less than the low value indicated in Figure 6b. Preferably, the signal classifier additionally has a sound / mute detector as indicated on the left of Figure 6b. In the case of sound expression, a noise fill level very low or equal to zero is indicated / applied. However, in the case of deaf expression, where the indication of temporal distortion does not indicate a temporal distortion processing due to the fact that no tone was found, but where the signal classifier indicates expression content, the fill measurement of Noise is not handled, but a normal noise fill level is applied.

Preferably, the audio signal analyzer comprises a tone tracker to generate a tone indication such as a tone contour or an absolute tone of a time frame of the audio signal. Then, the manipulator is configured to reduce the noise fill measurement when a tone is found, and not reduce the noise fill measurement when a tone is not found.

As indicated in Figure 6a, a signal analyzer 600, when applied on the decoder side, is not performing a real signal analysis such as a tone tracker or a sound / mute detector, but the signal analyzer parses syntactically. the encoded audio signal in order to extract information about temporal distortion or information about signal classification. Therefore, the signal analyzer 600 can be implemented within the input interface 539 in the decoder of Figure 5b.

A further embodiment of the present invention will be discussed later with respect to Figures 7a-7e. For early expression where a part of sound expression begins after a portion of relative silent signals, the block switching algorithm could classify it as an accident and could choose short blocks for this particular frame, with a loss of coding gain in the signal segment that has a clear harmonic structure. Therefore, the sound / deaf classification of the tone tracker is used to detect sound starts and prevent the block switching algorithm from indicating a transient accident around the found start. This feature can also be associated with the signal classifier to avoid changing blocks in expression signals and allowing them in all other signals. In addition, more precise control of block change could be implemented not only by permitting or rejecting accident detection, but also by using a variable threshold for accident detection based on sound start and information on signal classification. In addition, the information can be used to detect accidents such as the sound starts mentioned above but instead of switching to short blocks, it uses long windows with short overlays, which remain the preferable spectral resolution but reduces the temporal region where previous echoes may arise or later. Figure 7d shows the typical behavior without adaptation, Figure 7e shows two different adaptation possibilities (low overlay and prevention windows).

An audio encoder according to an embodiment of the present invention functions to generate an audio signal such as the signal produced by the output interface 522 of Figure 5a. The audio encoder comprises a Audio signal analyzer such as time distortion analyzer 516 or a signal classifier 520 of Figure 5a. Generally, the audio signal analyzer analyzes whether a time frame of the audio signal has a harmonic or expression characteristic. For this purpose, the signal classifier 520 of Figure 5a may include a sound / mute detector 520a or an expression / non-expression detector 520b. Although not shown in Figure 7a, a temporary distortion analyzer such as the temporal distortion analyzer 516 of Figure 5a may also be provided, which may include a tone tracker instead of items 520a and 520b or in addition to these functionalities. . Additionally, the audio encoder comprises the window function controller 504 for selecting a window function depending on a harmonic or expression characteristic of the audio signal as determined by the audio signal analyzer. The window generator 502 then generates windows of the audio signal or, depending on a certain implementation, the audio signal distorted over time using the selected window function to obtain a window frame. This window frame is then further processed by a processor to obtain an encoded audio signal. The processor may comprise items 508, 510, 512 illustrated in Figure 5a or more or less functionalities of well-known audio encoders such as transform-based audio encoders or time domain based audio encoders comprising such an LPC filter. as expression encoders and, specifically, expression encoders implemented according to the AMR-WB + standard.

In a preferred embodiment, the window function controller 504 comprises a transient detector 700 for detecting a transient in the audio signal, wherein the window function controller is configured to change from a window function for a long block to a window function for a short block, when a transient is detected and the audio signal analyzer does not find a harmonic or expression characteristic. When, however, a transient is detected and the audio signal analyzer finds a harmonic or expression characteristic, then the window function controller 504 does not change to the short block window function. The outputs of the window function indicating a long window when no transient is obtained and a short window when the transient detector detects a transient is illustrated as 701 and 702 in Figure 7a. This normal procedure as executed by the well-known AAC encoder is illustrated in Figure 7d. In the voice start position, the transient detector 700 detects an increase in energy from one frame to the next frame and, therefore, changes from a long window 710 to short windows 712. In order to accommodate this change, a long stop window 714 is used, which has a first overlay portion 714a, a non-aliasing portion 714b (generation of foreign signal), a second shorter overlay portion 714c and a portion 716 zero that extends between the point and the point on the time axis indicated by samples 2048. Then, the sequence of short windows indicated in 712 is executed, which is then terminated by a long start window 718 having a portion Long overlay 718a that overlaps with the following long window not illustrated in Figure 7d. In addition, this window has a non-aliasing portion 718b, a short overlay portion 718c and a zero portion that extends between point 720 on the time axis to point 2048. This portion is a zero portion.

Normally, switching to short windows is useful to avoid pre-echoes that appear within a frame before the event of the transient which is the sound start position or, generally, the beginning of the expression or the beginning of a signal that has a harmonic content Generally, a signal has a harmonic content, when a tone tracker decides that the signal has a tone. Alternatively, there are other harmonicity measurements such as a tonality measurement above a certain minimum level along with the characteristic that prominent peaks have a harmonic relationship with each other. There is a plurality of other techniques to determine whether a signal is harmonic or not.

A disadvantage of short windows is that the frequency resolution decreases, since the temporal resolution is increased. For high quality coding of the expression and, specifically, portions of sound expression or portions having a strong harmonic content, a good frequency resolution is desired. Therefore, the audio signal analyzer illustrated in 516, 520 or 520a, 520b is operative to produce a deactivated signal for transient detector 700 so that a change to short windows is avoided when a sound expression segment is detected or a segment of signals that has a strong harmonic characteristic. This ensures that, in order to encode said portions of signals, a high frequency resolution is maintained. This is a balance between the pre-echoes on the one hand and the high quality coding and high resolution of the tone for the expression signal or the tone for a harmonic non-expression signal on the other hand. It has been discovered that it is much more disturbing when the harmonic spectrum is not precisely coded compared to any pre-echo that may appear. In order to further reduce pre-echoes, TNS processing is favored for said situation, which will be discussed in relation to Figures 8a and 8b.

In an alternative embodiment illustrated in Figure 7b, the audio signal analyzer comprises a sound / mute and / or expression / non-expression detector 520a, 520b. However, the transient detector 700 included in the window function controller is not fully activated / deactivated as in Figure 7a, but the threshold included in the transient detector is controlled using a threshold control signal 704. In this embodiment, transient detector 700 is configured to determine a quantitative characteristic of the audio signal and to compare the quantitative characteristic with the controllable threshold, where a transient is detected when the quantitative characteristic has a predetermined relationship with the controllable threshold. The quantitative characteristic may be a number that indicates the increase in energy from one block to the next block, and the threshold may be a certain increase in energy threshold. When the increase in energy from one block to the next is greater than the increase in energy threshold, then a transient is detected, so that, in this case, the predetermined ratio is a "greater than" ratio. In other embodiments, the predetermined ratio may also be a "less than" ratio, for example when the quantitative characteristic is an inverted increase in energy. In the embodiment of Figure 7b, the controllable threshold is controlled so that the possibility of a change to a window function for a short block is reduced, when the audio signal analyzer has found a harmonic or expression characteristic. In the realization of the energy increase, the threshold control signal 704 will result in an increase in the threshold so that changes to short blocks only occur when the increase in energy from one block to the next is a particularly high increase in energy.

In an alternative embodiment, the output signal of the sound / mute detector 520a or the expression / non-expression detector 520b can also be used to control the window function controller 504 such that instead of switching to a short block in a Start of expression, a change is made to a window function that is longer than the window function for the short block. This window function ensures a higher frequency resolution than a short window function, but has a shorter length than the long window function so that a good composition is obtained between the pre-echoes on the one hand and a resolution of enough frequency on the other hand. In an alternate embodiment, a change to a window function having a smaller overlap can be made as indicated by the shaded line in Figure 7e at 706. The window function 706 has a sample length 2048 as the long block , but this window has a zero portion 708 and a non-aliasing portion 710 so that a short overlay length 712 is obtained from window 706 to the corresponding window 707. Window function 707, again, has a zero portion to the left of region 712 and a non-aliasing portion to the right of region 712 in analogy to window function 710. This low overlay embodiment effectively results in a shorter length of time to reduce the pre-echoes due to the zero portion 706 and 707 of the window, but on the other hand it has a sufficient length due to the overlay portion 714 and 710 portion of non-aliasing so that a sufficient frequency resolution is maintained.

In a preferred MDCT implementation as implemented by the AAC encoder, maintaining a certain overlay provides the additional advantage that, on the decoder side, an overlay / aggregate processing can be performed which means that a crossfade class is performed. between blocks This effectively avoids "artifacts" (in this context refers to errors in conversion) blocking. Additionally, this overlay / aggregate feature provides the cross fade feature without increasing the bit rate, that is, a critically shown cross fade is obtained. In short windows or regular long windows, the overlay portion is a 50% overlay as indicated by the overlay portion 714. In the embodiment where the window function is long as the 2048 samples, the overlay portion is 50%, that is, 1024 samples. The window function that has a shorter overlay that is used to effectively generate windows of a start Expression or a start of a harmonic signal is preferably less than 50% and is, in the embodiment of Figure 7e, only samples 128, which is 1/16 of the total window length. Preferably, the overlap portions between 1/4 and 1/32 of the total length of the window function are used.

Figure 7c illustrates this embodiment, in which an exemplary sound / mute detector 520a controls a window shape selector included in the window function controller 504 in order to select a window shape with a short overlay as indicated in 749 or a window form with a long overlay as indicated in 750. The selection of one of both forms is implemented, when the sound / mute detector 500a emits a sound signal detected in 751, where the audio signal used for analysis can be the audio signal at input 500 in Figure 5a or a preprocessed audio signal such as an audio signal distorted over time or an audio signal that has been subjected to any other preprocessing functionality. Preferably, the window shape selector 504 in Figure 7c which is included in the window function controller 504 in Figure 5a only uses signal 751, when a transient detector included in the window function controller detects a transient and orders a change from a long window function to a short window function as discussed in relation to Figure 7a.

Preferably, the realization of the window function change is combined with an embodiment of the temporal noise format discussed in relation to Figures 8a and 8b. However, the realization of TNS (temporary noise format) can also be implemented are the realization of block changes.

The spectral energy compaction property of the time-distorted MDCT also influences the temporal noise formatting tool (TNS), because the TNS gain tends to decrease for time-distorted frames especially for some signal signals. expression. However it is desirable activate TNS, p. example to reduce the pre-echoes at the sound beginnings or sound deviations (see the adaptation of the block change), where no block change is desired but still the temporal envelope of the expression signal exhibits rapid changes. Typically, an encoder uses some measurement to see if the application of the TNS is beneficial for a certain frame, for example the prediction gain of the TNS filter when applied to the spectrum. A variable TNS gain threshold is then preferred, which is lower for segments with an active tone contour, so as to ensure that the TNS is more often active for said portions of critical signals such as sound starts. As for the other tools, this can also be complemented taking into account the signal classification.

The audio encoder according to this embodiment for generating an audio signal comprises a controllable temporary distorter such as the temporary distorter 506 for the temporary distortion of the audio signal to obtain an audio signal distorted over time. Additionally, a frequency / time converter 508 is provided to convert at least a portion of the time distorted audio signal into a spectral representation. The frequency / time converter 508 preferably implements an MDCT transform as known from the AAC encoder, but the frequency / time converter can also perform any other kind of transform such as a DCT, DST, DFT, FFT or MDST transform or can comprise a filter bank such as a QMF filter bank.

Additionally, the encoder comprises a step 510 of temporal noise format to perform prediction filtering on the frequency of the spectral representation according to the control order of the temporal noise format, where prediction filtering is not performed, when not There is the control order of the temporary noise format.

Additionally, the encoder comprises a temporary noise format controller to generate the control order of the temporary noise format based on the spectral representation.

Specifically, the temporal noise format controller is configured to increase the possibility of performing frequency prediction filtering, when the spectral representation is based on a time distorted time signal or to reduce the possibility of performing prediction filtering. on frequency, when the spectral representation is not based on a time signal distorted in time. The details of the temporary noise format controller are discussed in relation to Figure 8.

The audio encoder additionally comprises a processor to further process a prediction filtering result on the frequency to obtain the encoded signal. In one embodiment, the processor comprises the step of step 512 of the quantizer encoder illustrated in Figure 5a.

A step TNS 510 illustrated in Figure 5a is illustrated in detail in Figure 8. Preferably, the temporary noise format controller included in step 510 comprises a TNS gain calculator 800, a TNS decision maker 802 later connected and a signal generator 804 threshold control. Depending on the signal of the temporal distortion analyzer 516 or the signal classifier 520 or both, the threshold control signal generator 804 produces a threshold control signal 806 for the TNS decision maker. The TNS decision maker 802 has a controllable threshold, which is incremented or reduced according to the threshold control signal 806. The threshold in the TNS decision maker 802 is, in this embodiment, a TNS gain threshold. When the actually calculated TNS gain produced by block 800 exceeds the threshold, then the TNS control order requires TNS processing as output, while in the other case when the TNS gain is below the gain threshold of TNS, no TNS order is produced or a signal is produced that orders that TNS processing is not useful and will not be performed in this specific time frame.

The TNS gain calculator 800 receives, as an input, the spectral representation obtained from the time distorted signal. Typically, a time distorted signal will have a lower TNS gain, but on the other hand, or TNS processing due to the time noise format feature in the time domain is beneficial in the specific situation, where there is a sound signal. / harmonica that has undergone a temporary distortion operation. On the other hand, TNS processing is not useful in situations where the TNS gain is low, which means that the residual TNS signal on line 510b has the same energy or greater energy than the signal before step 510 of TNS. In a situation, where the energy of the residual TNS signal in line 510d is slightly less than the energy before the TNS step 510, the processing of TNS may not be advantageous, due to the bit reduction because the energy slightly smaller in the signal that is actually used by step 512 of the entropy / quantifier encoder is smaller than the bit increment introduced by the necessary transmission of the secondary TNS information 510a indicated in Figure 5a. Although one embodiment automatically changes in TNS processing for all frames, in which a distorted signal is input in the time indicated by the tone information of block 516 or information 520 of the block's signal classifier, a preferred embodiment also they keep the possibility of TNS processing deactivated, but only when the gain is really low or at least less than in the normal case, when no harmonic / expression signal is processed.

Figure 8b illustrates an implementation where three different threshold settings are implemented by the TNS decision threshold control signal generator 804/802. When there is no tone contour, and when the signal classifier indicates a dull expression or no expression, then the TNS decision threshold is adjusted to be in a normal state requiring a relatively high TNS gain to activate the TNS . When, however, a tone contour is detected, but the signal classifier does not indicate any expression or the sound / mute detector detects a dull expression, then the TNS decision threshold is set at a lower level, which means that Even when comparatively low TNS gains are calculated by block 800 in Figure 8a, however, TNS processing is activated.

In a situation, in which an active tone contour is detected and in which a sound expression is found, then, the TNS decision threshold is set at the same lower level or set at an even lower state so that even small TNS earnings are sufficient to activate TNS processing.

In one embodiment, the TNS gain controller 800 is configured to estimate a gain in quality or bit rate, when the audio signal is subjected to frequency prediction filtering. A TNS decision maker 802 compares the estimated gain with a decision threshold, and a TNS control information in favor of prediction filtering is produced by block 802, when the estimated gain is in a predetermined relationship with respect to the threshold. decision, where this predetermined relationship may be a "greater than" ratio, but it may also be a "less than" ratio for an inverted gain of TNS for example. As discussed, the temporary noise format controller is also configured to vary the decision threshold preferably using the threshold control signal 806 so that, for the same estimated gain, prediction filtering is activated, when the spectral representation It is based on the time distorted audio signal, and is not activated, when the spectral representation is not based on the time distorted audio signal. Normally, the sound expression will exhibit a tone contour, and the deaf expression such as fricative sounds or sibilant sounds will not exhibit a tone contour. However, there are non-expression signals, with strong harmonic content and, therefore, have a tone contour, although the expression detector does not detect the expression. Additionally, there is some expression about music or music about expression signals, which the audio signal analyzer (516 of Figure 5a for example) determines to have a harmonic content, but which are not detected by the signal classifier 520 as expression signal In such a situation, all processing operations for sound expression signals can also be applied and will also result in an advantage.

Subsequently, another preferred embodiment of the present invention is described with respect to an audio encoder for encoding an audio signal. This audio encoder is specifically useful in the context of bandwidth extension, but it is also useful in the encoder applications alone, where the audio encoder is adjusted to encode a certain number of lines in order to obtain a certain bandwidth limitation / low pass filtering operation. In applications not distorted in time, this bandwidth limitation by selecting a certain predetermined number of lines will result in a constant bandwidth, since the sampling frequency of the audio signal is constant. In situations, however, in which a temporary distortion processing such as block 506 is performed in Figure 5a, an encoder that depends on a fixed number of lines will result in a variable bandwidth by introducing strong artifacts not only noticeable. for trained listeners but also noticeable for untrained listeners.

The central AAC encoder typically encodes a fixed number of lines, zeroing all others above the maximum line. In the non-distorted case this leads to a low-pass effect with a constant cut-off frequency and therefore a constant bandwidth of the decoded AAC signal. In the case of the time distorted case, the bandwidth varies due to the variation of the local sampling frequency, a function of the local temporal distortion contour, which leads to audible artifacts. These artifacts can be reduced by adaptively choosing the number of lines, as a function of the local temporal distortion contour and their average sampling rate obtained, to be encoded in the central encoder depending on the local sampling frequency so that a constant average bandwidth after redistorsing over time in the decoder for all frames. An additional benefit is the saving of bits in the encoder.

The audio encoder according to this embodiment comprises the temporary distorter 506 to distort in time an audio signal that uses a variable temporal distortion feature. Additionally, a frequency / time converter 508 is provided to convert a time distorted audio signal into a spectral representation having a number of spectral coefficients. Additionally, a processor is used to process a variable number of spectral coefficients to generate the encoded audio signal, where this processor comprising block 512 of the quantizer / encoder of Figure 5a is configured to set a number of spectral coefficients for a frame. of the audio signal based on the temporal distortion characteristic for the frame so that the variation in width is reduced or eliminated of band represented by the processed number of frequency coefficients from frame to frame.

The processor implemented by block 512 may comprise a controller 1000 to control the number of lines, where the result of controller 1000 is that, with respect to a reliable number of lines in the case of a time frame that is being encoded without any distortion. temporal, a certain variable number of lines is added or discarded at the upper end of the spectrum. Depending on the implementation, the controller 1000 may receive information on the contour of tones in a certain frame 1001 and / or a local average sampling frequency in the frame indicated in 1002.

In Figures 9 (a) to 9 (e), the drawings on the right illustrate a certain bandwidth situation for certain contours of tones on a frame, where the contours of tones on the frame are illustrated in the respective drawings of left for temporal distortion and are illustrated in the middle drawings after temporal distortion, where a substantially constant tone characteristic is obtained. This is the objective of the functionality of the temporal distortion that, after the temporal distortion, the characteristic of the tone is as constant as possible.

The bandwidth 900 illustrates the bandwidth that is obtained when a certain number of lines are produced produced by a frequency / time converter 508 or produced by a TNS stage 510 of Figure 5a, and when an operation is not performed of temporal distortion, that is, when the temporary distorter 506 was deactivated, as indicated by the shaded line 507. When, however, a non-constant temporal distortion contour is obtained, and when this temporal distortion contour is brought to a higher pitch inducing an increase in the sampling rate (Figure 9 (a), (c)) the width spectrum band decreases with respect to a situation not distorted in time, normal. This means that the number of lines to be transmitted for this frame must be increased in order to balance this loss of bandwidth.

Alternatively, bringing the tone to a lower constant tone illustrated in Figure 9 (b) or Figure 9 (d) results in a reduction in the sampling rate. The reduction of the sampling rate results in an increase in the bandwidth of the spectrum of this frame with respect to the linear scale, and this increase in bandwidth must be balanced using a suppression or disposal of a certain number of lines with respect to to the value of the number of lines for the normal situation not distorted in time.

Figure 9 (e) illustrates a special case, in which a tone contour is brought to a medium level so that the average sampling frequency within a frame is, instead of performing the time distortion operation, the same as The sampling frequency without any temporal distortion. In this way, the signal bandwidth is not affected, and the simple number of lines to be used for the normal case can be processed without temporary distortion, even if the temporary distortion operation has to be performed. From Figure 9, it becomes clear that performing a temporary distortion operation does not necessarily influence bandwidth, but that the influence on bandwidth depends on the tone contour and shape, as the temporal distortion is carried out. in a frame. Therefore, it is preferable to use, as a control value, a local or average sampling rate. The determination of this local sampling rate is illustrated in Figure 11. The upper portion in Figure 11 illustrates a temporary portion with equidistant sampling values. A framework includes, for example, seven sampling values indicated by Tn on the top line. The bottom line shows the result of a temporary distortion operation, in which, totally, there has been an increase in the sampling rate. This means that the length of the frame distorted in time is smaller than the temporal length of the frame not distorted in time. However, because the time frame length distorted in time to be introduced into the frequency / time converter is fixed, the case of an increase in the sampling rate causes an additional portion of the time signal that does not belong to the frame indicated by Tn be introduced into the frame distorted in time as indicated by lines 1100. Thus, a frame distorted in time covers a temporary portion of the audio signal indicated by Tlin which is larger than the Tn time. In view of this, the effective distance between two frequency lines or the frequency bandwidth of a single line in the linear domain (which is the inverse value for resolution) has decreased, and the number of fixed Nn lines for a case not distorted in time when multiplied by the reduced frequency distance results in a smaller bandwidth, that is, a reduction in bandwidth.

The other case, not illustrated in Figure 11, where the temporal distorter performs a reduction in the sampling rate, the effective time length of a frame in the time-distorted domain is smaller than the temporal length of the domain not distorted over time so that the frequency bandwidth of a single line or the distance between two frequency lines has increased. Now, multiplying this increased Af by the number N ⁿ of lines for the normal case will result in an increased bandwidth due to the reduced frequency resolution / increased frequency distance between two adjacent frequency coefficients.

Figure 11 further illustrates how an average sampling rate fsR is calculated. For this purpose, the temporal distance between two samples distorted in time is determined and the inverse value is taken, which is defined as is the local sampling rate between two samples distorted over time. Said value can be calculated between each pair of adjacent samples, and the arithmetic average value can be calculated and this value ultimately results in the local average sampling rate, which is preferably used to be entered into the controller 1000 of Figure 10a.

Figure 10b illustrates a line that indicates how many lines should be added or discarded depending on the local sampling frequency, where the sampling frequency fN for the non-distorted case along with the number of lines N ⁿ for the case not distorted over time defines the expected bandwidth, which must be kept constant as much as possible for a sequence of frames distorted in time or for a sequence of frames distorted in time and not distorted in time.

Figure 12b illustrates the dependence between the different parameters discussed in relation to Figure 9, Figure 10b and Figure 11. Basically, when the sampling rate, that is, the average sampling rate fSR decreases with respect to the case not distorted in over time, the lines should be suppressed, while the lines should be added, when the sampling rate increases with respect to the normal sampling rate fN for the case not distorted over time so that the frame bandwidth variations to frame are reduced or preferably still removed as much as possible.

The bandwidth that results in the number of lines N ⁿ and the sampling rate fN preferably defines the crossover frequency 1200 for an audio encoder that, in addition to a central source audio encoder, has an extension width encoder of band (BWE encoder). As is known in the art, a bandwidth extension encoder only encodes a spectrum with a high bit rate up to the crossover frequency and encodes the high band spectrum, that is, between the crossover frequency 1200 and the fMAX frequency with a low bit rate, where this low bit rate is typically still less than 1/10 or less of the bit rate required for the low band between a frequency of 0 and the crossover frequency 1200. Figure 12a further illustrates the BW ^aac bandwidth of a simple AAC audio encoder, which is much greater than the crossover frequency. Therefore, the lines can not only be discarded, but can also be added. In addition, the variation in bandwidth for a constant number of lines is also illustrated depending on the local sampling rate fSR. Preferably, the number of lines to be added or deleted with respect to the number of lines for the normal case is set so that each frame of the AAC encoded data has a maximum frequency as close as possible to the crossover frequency 1200 In this way, any spectral hole is avoided due to the reduction of bandwidth on one side or an unused free space by transmitting information at a frequency above the crossover frequency in the low band encoded frame. This, on the one hand, increases the quality of the decoded audio signal and, on the other hand, decreases the bit rate.

The actual addition of lines with respect to a fixed number of lines or a suppression of lines with respect to the fixed number of lines can be done before quantifying the lines, that is, at the entrance of block 512, or it can be done after quantifying or It can also be done, depending on the specific entropy code, after entropy coding.

In addition, it is preferable to bring bandwidth variations to a minimum level and still eliminate bandwidth variations, but, in other implementations, still a reduction in bandwidth variations by determining the number of lines that depend on the Temporal distortion feature still increases audio quality and reduces the required bit rate compared to a situation, where a constant number of lines is applied regardless of a certain temporal distortion characteristic.

Although some aspects have been described in the context of a team, it is clear that these aspects also represent a description of the corresponding method, where a block or device corresponds to a method step or a characteristic of the method step. Similarly, the aspects described in the context of a method step represent a description of a corresponding block or item or characteristic of the corresponding equipment.

Depending on certain implementation requirements, embodiments of the invention can be implemented in hardware or software. The implementation can be performed using a digital storage medium, for example a floppy disk, a DVD, a CD, a ROM, a PROM, an EPROM, an EEPROM or a quick memory, which has electronically readable control signals stored therein, cooperating (or being able to cooperate) with a programmable computer system in such a way that the respective method is carried out. Some embodiments according to the invention comprise a data carrier that has electronically readable control signals, which are capable of cooperating with a programmable computer system, such that one of the methods described herein is carried out. Generally, the embodiments of the present invention can be implemented as a computer program product with a program code, the program code that is operative to perform one of the methods when the computer program product operates on the computer. The program code can for example be stored on a machine readable media. Other embodiments include the computer program for carrying out one of the methods described. here, stored in the machine readable support. In other words, an embodiment of the inventive method is, therefore, a computer program that has a program code to carry out one of the methods described herein, when the computer program operates on a computer. A further embodiment of the inventive methods is, therefore, a data carrier (or a digital storage medium, or a computer readable medium) comprising, recorded therein, the computer program carried out by one of the methods described herein. A further embodiment of the inventive method is, therefore, a data stream or a sequence of signals representing the computer program for performing one of the methods described herein. The data stream or the signal sequence can for example be configured to be transferred through a data communication connection, for example over the Internet. A further embodiment comprises a processing means, for example a computer, or a programmable logic device, configured for or adapted to perform one of the methods described herein. A further embodiment comprises a computer that has the computer program installed therein to perform one of the methods described herein. In some embodiments, a programmable logic device (for example an array of field programmable gates) can be used to perform some or all of the functionalities of the methods described herein. In some embodiments, an array of programmable field doors may cooperate with a microprocessor in order to carry out one of the methods described herein.

Claims (1)

Audio encoder to generate an encoded audio signal, comprising: an audio signal analyzer (516, 520) to analyze whether a time frame of the audio signal has a harmonic or expression characteristic; a window function controller (504) for selecting a window function depending on a harmonic or expression characteristic of the audio signal; a window generator (502) for generating windows of the audio signal using the window function selected to obtain a window frame; Y a processor (508, 512) to further process the window frame to obtain the encoded audio signal; wherein the window function controller (504) comprises a transient detector (700) to detect a transient, in which the window function controller is configured to switch from a window function for a large block to a function of window for a short block, when a transient is detected and the audio signal analyzer (516, 520) does not find a harmonic or expression characteristic, and not to switch to the window function for the short block, when it is detected a transient and the audio signal analyzer (516, 520) finds a harmonic or expression characteristic; Y wherein the window function controller (504) is configured to switch from a window function (706) that has a sample length 2048 as the long block, but has a zero portion (708) and a portion (710 ) from not aliasing to a window function (707) that is longer than the window function for a short block and is adapted to obtain a shorter overlay length (712) with a window (706) prior to the function ( 714) window for a long block and having a zero portion to the left of a region (712) and a non-aliasing portion to the right of the region (712), when a transient is detected and the signal has a characteristic harmonic or expression, so that there is an overlap portion between 1/4 and 1/32 of the full window function length. Audio encoder to generate an encoded audio signal, comprising: an audio signal analyzer (516, 520) to analyze whether a time frame of the audio signal has a harmonic or expression characteristic; a window function controller (504) for selecting a window function depending on a harmonic or expression characteristic of the audio signal; a window generator (502) to generate windows to the audio signal using the window function selected to obtain a window frame; a processor (508, 512) to further process the window frame to obtain the encoded audio signal, and a transient detector; wherein the transient detector (700) is configured to detect a quantitative characteristic of the audio signal and to compare the quantitative characteristic with a controllable threshold, in which a transient is detected when the quantitative characteristic has a predetermined relationship with the controllable threshold, and wherein the audio signal analyzer is configured to control the variable threshold so that a probability of a switching to a window function for a short block is reduced, when the audio signal analyzer (516, 520) has found a harmonic or expression characteristic. Method for generating an encoded audio signal, comprising: analyze (516, 520) if the time frame of the audio signal has a harmonic or expression characteristic; select (504) a window function depending on a harmonic or expression characteristic of the audio signal; generate (502) windows to the audio signal using the selected window function to obtain a frame with windows; Y process (508, 512) the frame with windows to obtain the encoded audio signal; in which a switching of a window function for a long block to a window function for a short block is performed, when a transient is detected and a harmonic or expression characteristic is not found by analysis, and in which a switching of a window function (706) is performed which has a sample length 2048 as the long block, but which has a zero portion (708) and a portion (710) of non-aliasing to a function (707 ) of window that is longer than the window function for a short block and that has a shorter overlay (712) with a previous window (706) than the window function (714) for a long block and that has a portion zero to the left of an overlap region 712 and a non-aliasing portion to the right of the overlay region (712), when a transient is detected and the signal has a harmonic or expression characteristic, so that there is a portion overlap between 1/4 and 1/32 of the full window function length. Method for generating an encoded audio signal, comprising: analyze (516, 520) if a time frame of the audio signal has a harmonic or expression characteristic; select (504) a window function depending on a harmonic or expression characteristic of the audio signal; generate (502) windows to the audio signal using the selected window function, to obtain a frame with windows; Y process (508, 512) the frame with windows to obtain the encoded audio signal; in which a quantitative characteristic of the audio signal is detected and the quantitative characteristic is compared with a controllable threshold, in which a transient is detected when the quantitative characteristic has a predetermined relationship with the controllable threshold, and wherein the variable threshold is controlled so that a probability of a switching to a window function for a short block is reduced, when a harmonic or expression characteristic has been found. Computer program having a program code to perform, when executed on a computer, the method of claims 3 or 4.